ML Ops Engineer

Job Description

Responsibilities Contribute to the industrialization of the ML & GenAI technical stacks of our AI Platform, by participating in their definition, architecture, development, test, documentation & maintenance phases. Ensure the development process of this stack follows the DevSecOps recommendations & best practices . Write ML pipelines to illustrate the AI Platform capabilities. Contribute to the CI/CD workflows developments to automate the integration and deployment of the various ML assets (code or models) into the different environments. Contribute to the definition of the LLMOps / MLOps operating model and make sure the AI Platform enforces this model. Collaborate with our internal stakeholders to co-design the new features of the AI Platform . Occasionally be embarked into one of our stakeholder teams to help them using the AI platform, write ML pipelines, and, in return, collect platform evolution needs. Occasionally contribute to the collective effort of run & support for the AI Platform. Qualifications Requirements and Skills 5+ years of experience in ML Engineering. Strong knowledge on Machine Learning (including ML model management context) and GenAI technologies (including RAG). Previous hands-on experience on MLOps or LLMOps disciplines is mandatory . Good knowledge of DevOps processes at scale and experience of production systems. Rigorous mindset (but still open to changes), oriented towards customers, problems solving & results. Good team player . Autonomous . Ideally, some knowledge of Energy Management and Automation market . At ease in an international work environment & fluent in English . Familiar with agile methodologies: Scrum, SAFe. Technologies: Azure and/or AWS cloud technologies and their respective ML platforms (AzureML, AWS SageMaker). Databricks. GitHub, Terraform. ML Tools such as MLFlow. GenAI & LLM tools